x 轴上具有多个逻辑变量的堆积条形图答案

【问题标题】：Stacked bar plot with multiple logical variables in x axisx 轴上具有多个逻辑变量的堆积条形图
【发布时间】：2018-05-02 00:10:43
【问题描述】：

我正在处理时间序列数据。我有 16 个时间点和 3 个模型的视野。我为每个模型执行了预测误差方差分解，我想并排绘制每个模型的给定变量的 FEVD。我不知道我是否清楚，但假设在时间 1 中，模型 1 为 0%，模型 2 为 5%，模型 3 为 3%。我想在每个时间段内为每个模型绘制单独的条形图. ggplot2 可以吗？

下面是我的数据库示例：

Horizon Variable    Response  Shock Country  Model
   1      GDP     0.000000000  PCOM  Brazil Model 1
   2      GDP     0.404381850  PCOM  Brazil Model 1
   3      GDP     0.401069156  PCOM  Brazil Model 1
   4      GDP     0.368749090  PCOM  Brazil Model 1
   5      GDP     0.351268777  PCOM  Brazil Model 1
   6      GDP     0.345947281  PCOM  Brazil Model 1
   7      GDP     0.347482783  PCOM  Brazil Model 1
   8      GDP     0.352164160  PCOM  Brazil Model 1
   9      GDP     0.357781202  PCOM  Brazil Model 1
  10      GDP     0.363198705  PCOM  Brazil Model 1
  11      GDP     0.367974083  PCOM  Brazil Model 1
  12      GDP     0.372078699  PCOM  Brazil Model 1
  13      GDP     0.375666736  PCOM  Brazil Model 1
  14      GDP     0.378901315  PCOM  Brazil Model 1
  15      GDP     0.381878427  PCOM  Brazil Model 1
  16      GDP     0.384630719  PCOM  Brazil Model 1
   1      GDP     0.000000000  PCOM  Brazil Model 2
   2      GDP     0.301533139  PCOM  Brazil Model 2
   3      GDP     0.308349733  PCOM  Brazil Model 2
   4      GDP     0.263588570  PCOM  Brazil Model 2
   5      GDP     0.239982463  PCOM  Brazil Model 2
   6      GDP     0.235266964  PCOM  Brazil Model 2
   7      GDP     0.240041605  PCOM  Brazil Model 2
   8      GDP     0.248219530  PCOM  Brazil Model 2
   9      GDP     0.256646193  PCOM  Brazil Model 2
  10      GDP     0.263902054  PCOM  Brazil Model 2
  11      GDP     0.269612632  PCOM  Brazil Model 2
  12      GDP     0.273995159  PCOM  Brazil Model 2
  13      GDP     0.277464105  PCOM  Brazil Model 2
  14      GDP     0.280368261  PCOM  Brazil Model 2
  15      GDP     0.282903588  PCOM  Brazil Model 2
  16      GDP     0.285144263  PCOM  Brazil Model 2
   1      GDP     0.000000000  PCOM  Brazil Model 3
   2      GDP     0.034171019  PCOM  Brazil Model 3
   3      GDP     0.024779691  PCOM  Brazil Model 3
   4      GDP     0.016802809  PCOM  Brazil Model 3
   5      GDP     0.011206834  PCOM  Brazil Model 3
   6      GDP     0.009575322  PCOM  Brazil Model 3
   7      GDP     0.008935842  PCOM  Brazil Model 3
   8      GDP     0.008605141  PCOM  Brazil Model 3
   9      GDP     0.008182777  PCOM  Brazil Model 3
  10      GDP     0.007498230  PCOM  Brazil Model 3
  11      GDP     0.006684634  PCOM  Brazil Model 3
  12      GDP     0.005917865  PCOM  Brazil Model 3
  13      GDP     0.005320365  PCOM  Brazil Model 3
  14      GDP     0.004940644  PCOM  Brazil Model 3
  15      GDP     0.004782973  PCOM  Brazil Model 3
  16      GDP     0.004831577  PCOM  Brazil Model 3

编辑按照@A.Suliman 的建议，我通过以下方式更改了我的数据：

Data %>% mutate(Models = Model) %>% unite(Shocks, Shock, Model)

然后绘制：

gdp_br <- filter(Data, Variable  == "GDP")
xticks <- seq(min(0), max(16), by = 1)

ggplot(gdp_br, aes(as.factor(Horizon), Response, fill = Shocks, group = Models)) + 
  geom_bar(stat = "identity", width = 0.7, position = position_dodge(width = 0.8)) + 
  theme(plot.title = element_text(size = 10, face = "bold", lineheight = 1, hjust = 0), 
        axis.text.x = element_text(size = rel(1.1), angle = 10),
        legend.position = "bottom",
        legend.title = element_blank()) + 
  scale_y_continuous(labels = percent_format()) + 
  labs(x = "Horizon")

剧情是

但似乎有些标签没有被绘制出来。

EDIT2：我已经设法在 Excel 中获得所需的绘图。如何用 ggplot 绘制？

【问题讨论】：

是的，可以在 ggplot2 中执行此操作。到目前为止，您尝试过什么？
您在 Excel 中所需的绘图有一个 WGDP / VIX / PCOM 的填充图例，但据我所知，示例数据仅包含 PCOM。你不妨看看here。提供一个反映您的问题的数据样本，并显示您想要的结果是什么，基于该样本。

标签： r ggplot2 geom-bar

【解决方案1】：

library(ggplot2)
library(scales)

ggplot(Data, aes(as.factor(Horizon), Response,fill= Model)) +   
geom_bar( stat="identity", width = 0.7, position = position_dodge(width = 0.8)) +
  theme(plot.title = element_text(size = 10, face = "bold", lineheight=1,hjust = 0), axis.text.x = element_text( size = rel(1.1), angle = 10),legend.position = "bottom",legend.title = element_blank()) + scale_y_continuous(labels = percent_format()) +
  labs(
     x = "Horizon"
    #y = "Percentages",
    #title = gg_title,
    #subtitle = gg_title_subtitle
    #caption = "Data from fueleconomy.gov"
 )

数据

Input = ("
Horizon Variable    Response  Shock Country  Model
1      GDP     0.000000000  PCOM  Brazil 'Model 1'
2      GDP     0.404381850  PCOM  Brazil 'Model 1'
3      GDP     0.401069156  PCOM  Brazil 'Model 1'
4      GDP     0.368749090  PCOM  Brazil 'Model 1'
5      GDP     0.351268777  PCOM  Brazil 'Model 1'
6      GDP     0.345947281  PCOM  Brazil 'Model 1'
7      GDP     0.347482783  PCOM  Brazil 'Model 1'
8      GDP     0.352164160  PCOM  Brazil 'Model 1'
9      GDP     0.357781202  PCOM  Brazil 'Model 1'
10      GDP     0.363198705  PCOM  Brazil 'Model 1'
11      GDP     0.367974083  PCOM  Brazil 'Model 1'
12      GDP     0.372078699  PCOM  Brazil 'Model 1'
13      GDP     0.375666736  PCOM  Brazil 'Model 1'
14      GDP     0.378901315  PCOM  Brazil 'Model 1'
15      GDP     0.381878427  PCOM  Brazil 'Model 1'
16      GDP     0.384630719  PCOM  Brazil 'Model 1'
1      GDP     0.000000000  PCOM  Brazil 'Model 2'
2      GDP     0.301533139  PCOM  Brazil 'Model 2'
3      GDP     0.308349733  PCOM  Brazil 'Model 2'
4      GDP     0.263588570  PCOM  Brazil 'Model 2'
5      GDP     0.239982463  PCOM  Brazil 'Model 2'
6      GDP     0.235266964  PCOM  Brazil 'Model 2'
7      GDP     0.240041605  PCOM  Brazil 'Model 2'
8      GDP     0.248219530  PCOM  Brazil 'Model 2'
9      GDP     0.256646193  PCOM  Brazil 'Model 2'
10      GDP     0.263902054  PCOM  Brazil 'Model 2'
11      GDP     0.269612632  PCOM  Brazil 'Model 2'
12      GDP     0.273995159  PCOM  Brazil 'Model 2'
13      GDP     0.277464105  PCOM  Brazil 'Model 2'
14      GDP     0.280368261  PCOM  Brazil 'Model 2'
15      GDP     0.282903588  PCOM  Brazil 'Model 2'
16      GDP     0.285144263  PCOM  Brazil 'Model 2'
1      GDP     0.000000000  PCOM  Brazil 'Model 3'
2      GDP     0.034171019  PCOM  Brazil 'Model 3'
3      GDP     0.024779691  PCOM  Brazil 'Model 3'
4      GDP     0.016802809  PCOM  Brazil 'Model 3'
5      GDP     0.011206834  PCOM  Brazil 'Model 3'
6      GDP     0.009575322  PCOM  Brazil 'Model 3'
7      GDP     0.008935842  PCOM  Brazil 'Model 3'
8      GDP     0.008605141  PCOM  Brazil 'Model 3'
9      GDP     0.008182777  PCOM  Brazil 'Model 3'
10      GDP     0.007498230  PCOM  Brazil 'Model 3'
11      GDP     0.006684634  PCOM  Brazil 'Model 3'
12      GDP     0.005917865  PCOM  Brazil 'Model 3'
13      GDP     0.005320365  PCOM  Brazil 'Model 3'
14      GDP     0.004940644  PCOM  Brazil 'Model 3'
15      GDP     0.004782973  PCOM  Brazil 'Model 3'
16      GDP     0.004831577  PCOM  Brazil 'Model 3'
")

Data = read.table(textConnection(Input),header=TRUE)

 ggplot(Data,aes(Model, Response, fill=Shock)) + 
    geom_bar( stat = "identity", position = "stack") +
    facet_grid(~ Horizon, scales = "free_x", space = "free_x") +
    theme_bw() + 
    theme(panel.spacing = unit(0,"lines"),
    strip.background = element_blank(),plot.title = element_text(size = 10, face = "bold", lineheight=1,hjust = 0), axis.text.x = element_text( size = rel(1.1), angle = 90),legend.position = "bottom") + scale_y_continuous(labels = percent_format())

数据 2

#Using dput(Data)

Data <- structure(list(Horizon = c(1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 
10L, 11L, 12L, 13L, 14L, 15L, 16L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 
8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 1L, 2L, 3L, 4L, 5L, 
6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 1L, 2L, 3L, 
4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 1L, 
2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 
16L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 
14L, 15L, 16L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 
12L, 13L, 14L, 15L, 16L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 
10L, 11L, 12L, 13L, 14L, 15L, 16L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 
8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L), Variable = structure(c(1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = "GDP", class = "factor"), 
Response = c(0, 0.40438185, 0.401069156, 0.36874909, 0.351268777, 
0.345947281, 0.347482783, 0.35216416, 0.357781202, 0.363198705, 
0.367974083, 0.372078699, 0.375666736, 0.378901315, 0.381878427, 
0.384630719, 0, 0.301533139, 0.308349733, 0.26358857, 0.239982463, 
0.235266964, 0.240041605, 0.24821953, 0.256646193, 0.263902054, 
0.269612632, 0.273995159, 0.277464105, 0.280368261, 0.282903588, 
0.285144263, 0, 0.034171019, 0.024779691, 0.016802809, 0.011206834, 
0.009575322, 0.008935842, 0.008605141, 0.008182777, 0.00749823, 
0.006684634, 0.005917865, 0.005320365, 0.004940644, 0.004782973, 
0.004831577, 0.1, 0.50438185, 0.501069156, 0.46874909, 0.451268777, 
0.445947281, 0.447482783, 0.45216416, 0.457781202, 0.463198705, 
0.467974083, 0.472078699, 0.475666736, 0.478901315, 0.481878427, 
0.484630719, 0.1, 0.401533139, 0.408349733, 0.36358857, 0.339982463, 
0.335266964, 0.340041605, 0.34821953, 0.356646193, 0.363902054, 
0.369612632, 0.373995159, 0.377464105, 0.380368261, 0.382903588, 
0.385144263, 0.1, 0.134171019, 0.124779691, 0.116802809, 
0.111206834, 0.109575322, 0.108935842, 0.108605141, 0.108182777, 
0.10749823, 0.106684634, 0.105917865, 0.105320365, 0.104940644, 
0.104782973, 0.104831577, 0.2, 0.60438185, 0.601069156, 0.56874909, 
0.551268777, 0.545947281, 0.547482783, 0.55216416, 0.557781202, 
0.563198705, 0.567974083, 0.572078699, 0.575666736, 0.578901315, 
0.581878427, 0.584630719, 0.2, 0.501533139, 0.508349733, 
0.46358857, 0.439982463, 0.435266964, 0.440041605, 0.44821953, 
0.456646193, 0.463902054, 0.469612632, 0.473995159, 0.477464105, 
0.480368261, 0.482903588, 0.485144263, 0.2, 0.234171019, 
0.224779691, 0.216802809, 0.211206834, 0.209575322, 0.208935842, 
0.208605141, 0.208182777, 0.20749823, 0.206684634, 0.205917865, 
0.205320365, 0.204940644, 0.204782973, 0.204831577), Shock = structure(c(3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L), .Label = c("AAA", "BBB", 
"PCOM"), class = "factor"), Country = structure(c(1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = "Brazil", class = "factor"), 
Model = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L), .Label = c("Model 1", 
"Model 2", "Model 3"), class = "factor")), .Names = c("Horizon", 
"Variable", "Response", "Shock", "Country", "Model"), 
row.names = c(NA,-144L), class = "data.frame")

有关在 X 轴中标记两个变量的更多想法，check here。我没有在facet_grid 中定义switch = x，因为x 轴标签将低于构面变量，如here 所示，我认为这并不酷。

【讨论】：